Results 21 to 30 of about 392 (92)
, 2016 A new line of research on the lasso exploits the beautiful geometric fact that the lasso fit is the residual from projecting the response vector $y$ onto a certain convex polytope.
Harris, Naftali, Sepehri, Amir
core +1 more source
Optimization of ridge parameters in multivariate generalized ridge regression by plug-in methods
, 2012 Generalized ridge (GR) regression for a univariate linear model was proposed simultaneously with ridge regression by Hoerl and Kennard (1970). In this paper, we deal with a GR regression for a multivariate linear model, referred to as a multivariate GR ...
Isamu Nagai, H. Yanagihara, K. Satoh
semanticscholar +1 more source
Orthogonalized smoothing for rescaled spike and slab models
, 2008 Rescaled spike and slab models are a new Bayesian variable selection method for linear regression models. In high dimensional orthogonal settings such models have been shown to possess optimal model selection properties.
Ishwaran, Hemant, Papana, Ariadni
core +1 more source
The Influence Function of Penalized Regression Estimators [PDF]
, 2014 To perform regression analysis in high dimensions, lasso or ridge estimation are a common choice. However, it has been shown that these methods are not robust to outliers.
Alfons, Andreas +2 more
core +2 more sources
Normalized and standard Dantzig estimators: Two approaches
, 2015 We reconsider the definition of the Dantzig estimator and show that, in contrast to the LASSO, standardization of an experimental matrix leads in general to a different estimator than in the case when it is based on the original data.
J. Mielniczuk, Hubert Szymanowski
semanticscholar +1 more source
Asymptotic oracle properties of SCAD-penalized least squares estimators
, 2007 We study the asymptotic properties of the SCAD-penalized least squares estimator in sparse, high-dimensional, linear regression models when the number of covariates may increase with the sample size.
Huang, Jian, Xie, Huiliang
core +1 more source
Fast learning rate of multiple kernel learning: Trade-off between sparsity and smoothness [PDF]
, 2013 We investigate the learning rate of multiple kernel learning (MKL) with $\ell_1$ and elastic-net regularizations. The elastic-net regularization is a composition of an $\ell_1$-regularizer for inducing the sparsity and an $\ell_2$-regularizer for ...
Sugiyama, Masashi, Suzuki, Taiji
core +2 more sources
Integrating Ridge-type regularization in fuzzy nonlinear regression
, 2012 In this paper, we deal with the ridge-type estimator for fuzzy nonlinear regression models using fuzzy numbers and Gaussian basis functions. Shrinkage regularization methods are used in linear and nonlinear regression models to yield consistent ...
R. Farnoosh, J. Ghasemian, O. S. Fard
semanticscholar +1 more source
Improving both domain and total area estimation by composition [PDF]
, 2004 In this article we propose small area estimators for both the small and large area parameters. When the objective is to estimate parameters at both levels, optimality is achieved by a sample design that combines fixed and proportional allocation. In such
Costa, Àlex, Satorra, A., Ventura, Eva
core +3 more sources
Piecewise linear regularized solution paths
, 2007 We consider the generic regularized optimization problem $\hat{\mathsf{\beta}}(\lambda)=\arg \min_{\beta}L({\sf{y}},X{\sf{\beta}})+\lambda J({\sf{\beta}})$. Efron, Hastie, Johnstone and Tibshirani [Ann. Statist.
Rosset, Saharon, Zhu, Ji
core +2 more sources